MedAI

Title: Integrating Pathology Images and Genomics Data for Cancer Grading
Speaker: Xiaohan Xing
Abstract:
In recent years, Artificial Intelligence (AI) technology has been widely applied to the analysis of multi-modal biomedical data, revolutionizing healthcare. Integrating morphological information from pathology slides with molecular information from genomics data enhances cancer grading accuracy and reliability. However, this
integration faces challenges: modality gap, modality imbalance, and modality missing. To address these, we developed a low-rank constraint-based method to bridge the gap between different modalities and capture cross-modal complementary information, a saliency-aware masking strategy to balance the contribution of different modalities, and a knowledge distillation framework to improve model performance when genomics data is missing. We validated the effectiveness of these methods on the TCGA GBMLGG dataset.
Speaker Bio:
Dr. Xiaohan Xing is a postdoctoral researcher at Stanford University, having obtained her Ph.D. from The Chinese University of Hong Kong in 2021. She has been extensively involved in research related to medical AI, focusing on disease diagnosis and survival prediction based on medical images, omics data, and the integration of multimodal data. Currently, she has published more than 20 papers in top-tier journals and conferences in the field. Among these, she is the first author of 12 papers in leading journals such as Proceedings of IEEE, TMI, Medical Image Analysis, Bioinformatics, and prominent conferences like MICCAI and BIBM. Her research contributions have gained worldwide recognition, and she was honored with the MICCAI Young Scientist Award in 2022.
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